ReviewCureus2025
Pathomics in Gastrointestinal Tumors: Research Progress and Clinical Applications.
Review in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- A Pathomics-Based Prognostic Model for Disease-Free Survival in Resected Gastric Cancer.Cancers · 2026Article
- Uncertainty-aware and causal test-time adaptive foundation model for robust colorectal cancer pathology diagnosis.NPJ digital medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gastrointestinal tumors are among the malignancies with the highest global incidence and mortality rates, and their diagnosis and treatment heavily rely on histopathological examination. However, traditional pathological assessment faces challenges such as strong subjectivity, heavy workload, and low diagnostic consistency. In recent years, with advancements in high-resolution digital slide scanning technology and the rapid development of deep learning algorithms, pathomics has emerged as a novel tool for the precise diagnosis and treatment of gastrointestinal tumors. By extracting high-throughput quantitative features from whole slide images and combining machine learning and deep learning techniques, pathomics enables automated tumor typing, prognosis prediction, and treatment response evaluation. This article reviews the research progress of pathomics in gastrointestinal tumors, focusing on its applications in gene mutation prediction, prognosis assessment, and treatment response prediction, while analyzing current challenges and future directions.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.